pacman::p_load(sf, raster, spatstat, sparr, tmap, tidyverse)TH1: Geospatial Analytics for Social Good
Take Home Exercise 1: Application of Spatial and Spatio-temporal Point Patterns Analysis to discover the geographical distribution of Armed Conflict in Myanmar
TH1.1 Setting the Scene
Millions of people have their lives shattered by armed conflict every year. One of these is the Myanmar Civil War, a significant escalation of the long-running Myanmar Conflict in response to the 2021 coup d’etat.
Geospatial analtyics holds the potential to address complex problems facing society, such as this one. This study serves to discover the sptial and spatio-temporal distribution (spread) of the latest armed conflict in Myanmar by applying spatial point pattern analysis (SPPA) methods.
TH1.1.1 Loading R packages
The R packages we will use today are:
sftmapspatstatsparrrastermaptools
TH1.2 The Data
Armed conflict data in Myanmar between 2010 and 2023 was downloaded from the Armed Conflict Location & Event Database (ACLED), an independent, impartial, international non-profit organization collecting data on violent conflicts and protests in all countries and territories in the world. We will be superimposing these locations with the geogrpahical boundary and subdivisions of the country, from the Myanmar Information Management Unit (MIMU).
We are interested in conflict events from 2019 onwards - starting from a year before the COVID-19 pandemic and 2 years before the 2021 coup - cumulating into the civil war of today.
TH1.2.1 Loading armed conflict data into tibble object
Here we load our aspatial armed conflict data into a tibble object.
acled_mya <- read_csv("data/2021-01-01-2024-09-16-Southeast_Asia-Myanmar.csv")Rows: 53653 Columns: 31
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (20): event_id_cnty, event_date, disorder_type, event_type, sub_event_ty...
dbl (11): year, time_precision, inter1, inter2, interaction, iso, latitude, ...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.head(acled_mya)# A tibble: 6 × 31
event_id_cnty event_date year time_precision disorder_type event_type
<chr> <chr> <dbl> <dbl> <chr> <chr>
1 MMR66329 06 September 2024 2024 1 Political vio… Battles
2 MMR66334 06 September 2024 2024 1 Political vio… Explosion…
3 MMR66353 06 September 2024 2024 1 Political vio… Explosion…
4 MMR66365 06 September 2024 2024 1 Political vio… Explosion…
5 MMR66368 06 September 2024 2024 1 Political vio… Battles
6 MMR66422 06 September 2024 2024 1 Political vio… Explosion…
# ℹ 25 more variables: sub_event_type <chr>, actor1 <chr>, assoc_actor_1 <chr>,
# inter1 <dbl>, actor2 <chr>, assoc_actor_2 <chr>, inter2 <dbl>,
# interaction <dbl>, civilian_targeting <chr>, iso <dbl>, region <chr>,
# country <chr>, admin1 <chr>, admin2 <chr>, admin3 <chr>, location <chr>,
# latitude <dbl>, longitude <dbl>, geo_precision <dbl>, source <chr>,
# source_scale <chr>, notes <chr>, fatalities <dbl>, tags <chr>,
# timestamp <dbl>TH1.2.2 Loading administrative boundaries into sf object
Three representations of Myanmar’s geography exist on the MIMU repository - Admin1 subdivides the country into its states and regions only, while Admin2 subdivides the country by its smaller districts, and Admin3 its townships. Further, the ACLED labels each incident with all 3 representations. For simplicity and ease of understanding, we shall use the Admin1 representation.
mmr_admin1 <- st_read(dsn = "data/mmr_polbnda_adm1_250k_mimu_1", layer = "mmr_polbnda_adm1_250k_mimu_1")Reading layer `mmr_polbnda_adm1_250k_mimu_1' from data source
`/Users/kendricktty/Gits/smu_cs/is415-site/TakeHome/TakeHome1/data/mmr_polbnda_adm1_250k_mimu_1'
using driver `ESRI Shapefile'
Simple feature collection with 15 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 92.1721 ymin: 9.696844 xmax: 101.17 ymax: 28.54554
Geodetic CRS: WGS 84mmr_admin1Simple feature collection with 15 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 92.1721 ymin: 9.696844 xmax: 101.17 ymax: 28.54554
Geodetic CRS: WGS 84
First 10 features:
OBJECTID ST ST_PCODE ST_RG ST_MMR PCode_V
1 1 Ayeyarwady MMR017 Region ဧရာဝတီတိုင်းဒေသကြီး 9.4
2 2 Bago MMR111 Region ပဲခူးတိုင်းဒေသကြီး 9.4
3 4 Chin MMR004 State ချင်းပြည်နယ် 9.4
4 5 Kachin MMR001 State ကချင်ပြည်နယ် 9.4
5 6 Kayah MMR002 State ကယားပြည်နယ် 9.4
6 7 Kayin MMR003 State ကရင်ပြည်နယ် 9.4
7 8 Magway MMR009 Region မကွေးတိုင်းဒေသကြီး 9.4
8 9 Mandalay MMR010 Region မန္တလေးတိုင်းဒေသကြီး 9.4
9 10 Mon MMR011 State မွန်ပြည်နယ် 9.4
10 11 Nay Pyi Taw MMR018 Union Territory နေပြည်တော် 9.4
geometry
1 MULTIPOLYGON (((95.20798 15...
2 MULTIPOLYGON (((96.17964 19...
3 MULTIPOLYGON (((93.36931 24...
4 MULTIPOLYGON (((97.59674 28...
5 MULTIPOLYGON (((97.1759 19....
6 MULTIPOLYGON (((97.81508 16...
7 MULTIPOLYGON (((94.11699 22...
8 MULTIPOLYGON (((96.14023 23...
9 MULTIPOLYGON (((97.73689 15...
10 MULTIPOLYGON (((96.32013 20...qtm(mmr_admin1) # Plots the national boundaries of Myanmar
TH1.2.3 Creating sf data frame from aspatial data
The ACLED tibble contains coordinates, making it useful for plotting on our map as points. We can therefore use it to create an sf data frame using which we can plot our points on a map. The EPSG format of the import coordinates should be 4326, corresponding to the WGS84 Geographic Coordinate System.
acled_mya_sf <- st_as_sf(acled_mya, coords = c("longitude", "latitude"), crs = 4326)TH1.3 A brief summary of Myanmar and the Myanmar Conflict
The second largest country in Southeast Asia at 676,578 square kilometres and an ASEAN member state, Myanmar features fertile tropical deltas in the south and the rugged Himalayan foothills to its north. It shares borders with China to the north and northeast, Laos and Thailand to the east and Southeast, and Bangladesh and India to the west and northwest. Despite having a 2800km coastline providing access to deep-sea ports and an abundance in natural resources including arable land, forests, minerals and natural gas, conflict-ridden Myanmar is considered a least developed country, with a GNP per capita of US$1144 (2011) (Myanmar Information Management Unit, n.d.) and a HDI of 0.6 (UNDP, 2024). Consequently, agriculture accounts for 36% of GDP and 60-70% of employment.
Myanmar is organised into seven states, seven regions, and one union territory - its capital, Naypyitaw, located at around the country’s geographic centre. The states - Chin, Kachin, Kayah, Kyain, Mon, Rakhine and Shan - are largely populated by the national ethnic communities, while most of the inhabitants of its regions - Ayeyarwady, Bago, Mandalay, Sagaing, Tanintharyi and largest city Yangon - are of the majority Bamar (Burmese) ethnicity. The following code chunk plots the states and regions of Myanmar into an interactive map.
tmap_mode('view')tmap mode set to interactive viewingtm_shape(mmr_admin1) + tm_polygons()tmap_mode('plot')tmap mode set to plottingCrucially, Myanmar is one of the world’s most ethnically diverse countries, with as many as 135 different ethnic groups boasting a rich tapestry of culture and religious history (Maizland, 2022). While this may paint a rosy picture of harmony on the surface, this is far from the case in the country. While the majority Bamar enjoy a privileged position in society and have held of government and military positions, many of the country’s minority groups grapple with systemic discrimination, lack of economic opportunity and development, minimum representation in government, and abuses at the hands of the military.
The code chunk below visualises Myanmar’s ethnic breakdown in a pie chart, based on data from the US CIA World Factbook and presented by Maizland (2022).
# Plot the composition of ethnic groups in Myanmar
x <- c(2, 2, 3, 4, 5, 7, 9, 68)
y <- c("Indian", "Mon", "Chinese", "Rakhine", "Other", "Karen", "Shan", "Bamar")
y <- paste(y, x)
y <- paste(y, "%", sep="")
pie(x, labels = y, main = "Composition of Ethnic Groups in Myanmar (Maizland, 2022)", col = c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7"))
# legend("topright", y, cex = 0.8, fill = c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7"))Because of this disparity and partially as a result of the divide-and-conquer strategy used by the British during colonial rule, lengthy armed conflicts have ensued since Myanmar’s independence between the ethnic groups (comprising ethnic armed organisations and smaller militias) and the Tatmadaw, the military, producing what has become known as the world’s longest continuing civil war (Rieffel, 2019), over issues from greater autonomy to control of natural resources. This did not stop even after the country underwent a democratic reform in the early 2010s - since 2017, the Tatmadaw had been mounting a brutal campaign against the ethnic Rohingya (Albert & Maizland, 2020). While the administration of then-state chancellor Aung San Suu Kyi defended their actions, restricting press freedoms in the process, rights groups and UN officials claim the Tatmadaw’s actions amounted to genocide.
TH1.4 Data wrangling
TH1.4.1 Coordinate system conversion
The EPSG area code for Myanmar is 4239, while the WGS 84-compatible code is 32647. Since we’re creating an owin object later, we need to first convert our coordinate system.
acled_mya_sf <- acled_mya_sf %>% st_transform(crs = 32647)
mmr_admin1 <- mmr_admin1 %>% st_transform(crs = 32647)
tm_shape(mmr_admin1) + tm_polygons() + tm_shape(acled_mya_sf) + tm_dots(size = 0.05)
TH1.4.2 Feature creation
Since the data is timestamped, we are able to plot and compare the frequency of conflict events over time. Before we do, though, we need to standardise the date stamps, then compartmentalise them into their year, month and day components, so that we can perform spatial-temporal point patterns analysis (STPPA) later. We will also aim to segregate events by whether or not they occurred before the coup on February 1, 2021.
A visualisation of the spatial-temporial distribution of points can be found in Annex A. Notice the consistent appearance throughout the entire study period of a large cluster of points towards the west of the country - roughly the location of Rahkine state on the border with Bangladesh.
Additionally, the ACLED dataset splits the conflict incidents in Shan state into North, South and East, and incidents in the Bago region into East and West. We need to combine these into
acled_mya_sf <- acled_mya_sf %>%
mutate(event_date = dmy(event_date)) %>%
mutate(DayOfYear = yday(event_date)) %>%
mutate(Month_num = format(event_date, "%Y-%m")) %>% # Year-Month format
mutate(Month_fac = factor(format(event_date, "%B %Y"))) %>% # Full month
mutate(Quarter_num = ((year(event_date) - 2021) * 4) + quarter(event_date)) %>% # Numbers quarters for STKDE
mutate(Quarter = paste(year(event_date), "-", quarter(event_date), "Q")) %>% # Quarter format
mutate(admin1 = case_when(
admin1 %in% c("Shan-North", "Shan-South", "Shan-East") ~ "Shan",
admin1 %in% c("Bago-East", "Bago-West") ~ "Bago",
TRUE ~ admin1
)) [1] "Kachin" "Shan" "Magway" "Sagaing" "Chin"
[6] "Rakhine" "Mandalay" "Tanintharyi" "Kayah" "Yangon"
[11] "Kayin" "Mon" "Ayeyarwady" "Bago" "Nay Pyi Taw" [1] 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1[1] 15 [1] "2024 - 3 Q" "2024 - 2 Q" "2024 - 1 Q" "2023 - 4 Q" "2023 - 3 Q"
[6] "2023 - 2 Q" "2023 - 1 Q" "2022 - 4 Q" "2022 - 3 Q" "2022 - 2 Q"
[11] "2022 - 1 Q" "2021 - 4 Q" "2021 - 3 Q" "2021 - 2 Q" "2021 - 1 Q"tm_shape(mmr_admin1) + tm_polygons() + tm_shape(acled_mya_sf) + tm_dots(size = 0.05)
tm_shape(mmr_admin1) + tm_polygons() + tm_shape(acled_mya_sf) + tm_dots(size = 0.05) + tm_facets(by="Quarter_num", free.coords=FALSE, drop.units = TRUE)
tail(acled_mya_sf)Simple feature collection with 6 features and 34 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 297846.6 ymin: 1920825 xmax: 389155.2 ymax: 2653913
Projected CRS: WGS 84 / UTM zone 47N
# A tibble: 6 × 35
event_id_cnty event_date year time_precision disorder_type event_type
<chr> <date> <dbl> <dbl> <chr> <chr>
1 MMR11022 2021-01-02 2021 1 Political violence Explosions/R…
2 MMR10936 2021-01-02 2021 1 Political violence Battles
3 MMR10933 2021-01-01 2021 1 Political violence Battles
4 MMR10920 2021-01-01 2021 1 Political violence Violence aga…
5 MMR10906 2021-01-01 2021 1 Political violence Battles
6 MMR10932 2021-01-01 2021 1 Political violence Battles
# ℹ 29 more variables: sub_event_type <chr>, actor1 <chr>, assoc_actor_1 <chr>,
# inter1 <dbl>, actor2 <chr>, assoc_actor_2 <chr>, inter2 <dbl>,
# interaction <dbl>, civilian_targeting <chr>, iso <dbl>, region <chr>,
# country <chr>, admin1 <chr>, admin2 <chr>, admin3 <chr>, location <chr>,
# geo_precision <dbl>, source <chr>, source_scale <chr>, notes <chr>,
# fatalities <dbl>, tags <chr>, timestamp <dbl>, geometry <POINT [m]>,
# DayOfYear <dbl>, Month_num <chr>, Month_fac <fct>, Quarter_num <dbl>, …TH1.4.3 Using owin to confine study area within Myanmar borders
Finally, we need to create an owin object that will be combined with any ppp objects we create later for SPPA.
mmr_admin1_owin <- as.owin(mmr_admin1)
plot(mmr_admin1_owin)
# summary(mmr_admin1_owin)TH1.5 Preliminary analysis
TH1.5.1 Exploratory Data Analysis (EDA)
We can quickly gain some meaningful insights from our dataset using basic EDA techniques. For instance, with the following code chunk, we can retrieve the total number of fatalities from the armed violence during the study period on a quarterly basis, and print its corresponding summary statistics.
TH1.5.1.1 Getting quarterly distribution of fatalities and summary statistics
acled_mya_fatalities_by_quarter <- acled_mya_sf %>%
group_by(Quarter_num) %>%
summarise(`All fatalities` = sum(fatalities)) %>%
ungroup()
acled_mya_fatalities_by_quarterSimple feature collection with 15 features and 2 fields
Geometry type: MULTIPOINT
Dimension: XY
Bounding box: xmin: -208804.4 ymin: 1103500 xmax: 640934.5 ymax: 3042960
Projected CRS: WGS 84 / UTM zone 47N
# A tibble: 15 × 3
Quarter_num `All fatalities` geometry
<dbl> <dbl> <MULTIPOINT [m]>
1 1 803 ((-203795.3 2372607), (-201788.2 2366580), (-19…
2 2 1939 ((-191409.1 2317222), (-174344.6 2321971), (-17…
3 3 2698 ((-193181.1 2333786), (-191409.1 2317222), (-17…
4 4 5542 ((-200024.3 2345207), (-199243.8 2366471), (-19…
5 5 5301 ((-204784 2358873), (-195914.6 2357792), (-1935…
6 6 5862 ((-206931.7 2359490), (-191409.1 2317222), (-17…
7 7 4803 ((-206196.6 2354710), (-205672.3 2353283), (-20…
8 8 3795 ((-206531.5 2357132), (-202593.1 2350220), (-20…
9 9 3951 ((-199243.8 2366471), (-191409.1 2317222), (-17…
10 10 3822 ((-191261.5 2351144), (-187696.5 2324700), (-17…
11 11 3485 ((-197883.4 2339678), (-196507.9 2341782), (-19…
12 12 4792 ((-206931.7 2359490), (-204784 2358873), (-2029…
13 13 3955 ((-207135 2358896), (-206931.7 2359490), (-1978…
14 14 3982 ((-208804.4 2357274), (-201925.4 2354103), (-19…
15 15 4299 ((-197883.4 2339678), (-193173 2319902), (-1929…summary(acled_mya_fatalities_by_quarter) Quarter_num All fatalities geometry
Min. : 1.0 Min. : 803 MULTIPOINT :15
1st Qu.: 4.5 1st Qu.:3640 epsg:32647 : 0
Median : 8.0 Median :3955 +proj=utm ...: 0
Mean : 8.0 Mean :3935
3rd Qu.:11.5 3rd Qu.:4798
Max. :15.0 Max. :5862 We can establish that 3935 people die from the armed violence each quarter.
TH1.5.1.2 Plotting quarterly distribution of fatalities as a line chart
ggplot(acled_mya_fatalities_by_quarter, aes(x=Quarter_num, y=`All fatalities`)) +
# geom_bar(stat = "identity", fill = "red4") +
geom_line(color = "red4") + geom_point() +
labs(title = "Total Fatalities from the Armed Conflict in Myanmar by Quarter", subtitle = "1 = Q1 2021, 15 = Q3 2024",
x = "Quarter",
y = "All Fatalities") +
theme_minimal()
There were fewer deaths from armed conflict in the first three quarters of 2021 - at the very start of the conflict - than Q4 2021 onwards.
TH1.5.1.3 Getting quarterly distribution of incidents involving civilian targeting
Using the same code structure, we can also plot the distribution of armed conflict events involving civilian targeting.
acled_mya_civilianTargeting_by_quarter <- acled_mya_sf %>%
group_by(Quarter_num) %>%
summarise(
Count = n(),
Civilians_targeted = sum(civilian_targeting == "Civilian targeting", na.rm = TRUE)
) %>%
mutate(Percentage_incidents_civilians_targeting = (Civilians_targeted / Count) * 100) %>%
ungroup()
acled_mya_civilianTargeting_by_quarterSimple feature collection with 15 features and 4 fields
Geometry type: MULTIPOINT
Dimension: XY
Bounding box: xmin: -208804.4 ymin: 1103500 xmax: 640934.5 ymax: 3042960
Projected CRS: WGS 84 / UTM zone 47N
# A tibble: 15 × 5
Quarter_num Count Civilians_targeted geometry
<dbl> <int> <int> <MULTIPOINT [m]>
1 1 3695 354 ((-203795.3 2372607), (-201788.2 236658…
2 2 4727 615 ((-191409.1 2317222), (-174344.6 232197…
3 3 3394 697 ((-193181.1 2333786), (-191409.1 231722…
4 4 4453 823 ((-200024.3 2345207), (-199243.8 236647…
5 5 4267 626 ((-204784 2358873), (-195914.6 2357792)…
6 6 4116 753 ((-206931.7 2359490), (-191409.1 231722…
7 7 3882 720 ((-206196.6 2354710), (-205672.3 235328…
8 8 3689 698 ((-206531.5 2357132), (-202593.1 235022…
9 9 3368 683 ((-199243.8 2366471), (-191409.1 231722…
10 10 2921 657 ((-191261.5 2351144), (-187696.5 232470…
11 11 3200 681 ((-197883.4 2339678), (-196507.9 234178…
12 12 3753 841 ((-206931.7 2359490), (-204784 2358873)…
13 13 3258 809 ((-207135 2358896), (-206931.7 2359490)…
14 14 2830 763 ((-208804.4 2357274), (-201925.4 235410…
15 15 2100 507 ((-197883.4 2339678), (-193173 2319902)…
# ℹ 1 more variable: Percentage_incidents_civilians_targeting <dbl>summary(acled_mya_civilianTargeting_by_quarter) Quarter_num Count Civilians_targeted geometry
Min. : 1.0 Min. :2100 Min. :354.0 MULTIPOINT :15
1st Qu.: 4.5 1st Qu.:3229 1st Qu.:641.5 epsg:32647 : 0
Median : 8.0 Median :3689 Median :697.0 +proj=utm ...: 0
Mean : 8.0 Mean :3577 Mean :681.8
3rd Qu.:11.5 3rd Qu.:3999 3rd Qu.:758.0
Max. :15.0 Max. :4727 Max. :841.0
Percentage_incidents_civilians_targeting
Min. : 9.581
1st Qu.:18.388
Median :20.279
Mean :19.629
3rd Qu.:22.451
Max. :26.961 ggplot(acled_mya_civilianTargeting_by_quarter, aes(x=Quarter_num, y=Percentage_incidents_civilians_targeting)) +
# geom_bar(stat = "identity", fill = "red4") +
geom_line(color = "skyblue1") + geom_point() +
labs(title = "Incidents involving civilian targeting in Myanmar by Quarter", subtitle = "1 = Q1 2021, 15 = Q3 2024",
x = "Quarter",
y = "Incidents with civilian targeting") +
theme_minimal()
The percentage of violent conflict incidents targeting civilians trended upwards throughout the study duration.
TH1.5.1.4 Plotting proportion of fatalities by type of conflict
We can also plot the proportion of fatalities by the type of conflict. The ACLED classifies armed conflict events by mode and intensity of attack and by whether civilians are targeted. From the graph and pie chart below, the largest proportion of fatalities comes from battles, closely followed by explosions and remote violence. However, violence against civilians makes up a sizeable 11.4% of fatalities over the study period.
unique(acled_mya_sf$event_type)[1] "Battles" "Explosions/Remote violence"
[3] "Strategic developments" "Protests"
[5] "Violence against civilians" "Riots" acled_mya_fatalities_by_event_type <- acled_mya_sf %>%
group_by(event_type) %>%
summarise(`All fatalities` = sum(fatalities)) %>%
ungroup()
acled_mya_fatalities_by_event_typeSimple feature collection with 6 features and 2 fields
Geometry type: MULTIPOINT
Dimension: XY
Bounding box: xmin: -208804.4 ymin: 1103500 xmax: 640934.5 ymax: 3042960
Projected CRS: WGS 84 / UTM zone 47N
# A tibble: 6 × 3
event_type `All fatalities` geometry
<chr> <dbl> <MULTIPOINT [m]>
1 Battles 39059 ((-207135 2358896), (-206931.7 23…
2 Explosions/Remote violence 12473 ((-208804.4 2357274), (-206931.7 …
3 Protests 381 ((-191409.1 2317222), (-174344.6 …
4 Riots 258 ((-21578.24 2371638), (275.0944 2…
5 Strategic developments 127 ((-206931.7 2359490), (-201788.2 …
6 Violence against civilians 6731 ((-206931.7 2359490), (-204784 23…ggplot(acled_mya_fatalities_by_event_type, aes(x=event_type, y=`All fatalities`)) +
geom_bar(stat = "identity", fill = "red4") +
labs(title = "Fatalities by event type in Myanmar",
x = "Quarter",
y = "All Fatalities") +
theme_minimal()
total_fatalities <- sum(acled_mya_fatalities_by_event_type$`All fatalities`)
percentages <- 100 * acled_mya_fatalities_by_event_type$`All fatalities` / total_fatalities
# Set plotting area size
par(mar = c(1, 1, 2, 1)) # Adjust margins if needed
pie(acled_mya_fatalities_by_event_type$`All fatalities`, labels = paste0(round(percentages, 1), "%"),
col = rainbow(length(acled_mya_fatalities_by_event_type$event_type)),
main = "Fatalities by event type in Myanmar",
cex = 1
)
legend("topright", paste(acled_mya_fatalities_by_event_type$event_type, ":", round(percentages, 1), "%"), fill = rainbow(length(acled_mya_fatalities_by_event_type$event_type)), cex = 0.7)
We can further narrow down the scope of this analysis by limiting our data to a certain year, and to events where the civilian_targeting column is filled. The year 2022 saw the highest fatality numbers across the entire study period, so we will take a closer look.
acled_mya_fatalities_by_event_type_2022 <- acled_mya_sf %>%
filter(year == "2022") %>%
filter(civilian_targeting == "Civilian targeting") %>%
group_by(event_type) %>%
summarise(`All fatalities` = sum(fatalities)) %>%
ungroup()
ggplot(acled_mya_fatalities_by_event_type_2022, aes(x=event_type, y=`All fatalities`)) +
geom_bar(stat = "identity", fill = "red4") +
labs(title = "Fatalities by event type in 2022 with civilian_targeting filled",
x = "Quarter",
y = "All Fatalities") +
theme_minimal()
TH1.5.1.5 Mapping fatalities and violence against civilians
Finally, we can plot choropleth maps displaying 2 crucial pieces of information: the locations with the greatest number of fatalities, and the locations where civilians are targeted in the violence the most in 2022. In the code chunks we will write to create the plots, we have to refer to the original tibble objects instead of the sf objects.
acled_mya_fatalities_by_admin1_2022 <- acled_mya %>%
filter(year == 2022) %>%
mutate(admin1 = case_when(
admin1 %in% c("Shan-North", "Shan-South", "Shan-East") ~ "Shan",
admin1 %in% c("Bago-East", "Bago-West") ~ "Bago",
TRUE ~ admin1
)) %>%
group_by(admin1) %>%
summarise(`All_Fatalities` = sum(fatalities)) %>%
ungroup()
combined <- left_join(mmr_admin1, acled_mya_fatalities_by_admin1_2022, by = c("ST" = "admin1"))
tm_shape(combined) +
tm_fill("All_Fatalities", n = 8, style = "kmeans") +
tm_borders(alpha = 0.5) +
tm_layout(title = "admin1 by number of fatalities, 2022", legend.outside = TRUE)
The above map shows Sagaing region as the location with the highest number of fatalities in 2022, followed by Magaway region.
#|echo: false
combinedSimple feature collection with 15 features and 7 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -210008.6 ymin: 1072026 xmax: 724647.6 ymax: 3158467
Projected CRS: WGS 84 / UTM zone 47N
First 10 features:
OBJECTID ST ST_PCODE ST_RG ST_MMR PCode_V
1 1 Ayeyarwady MMR017 Region ဧရာဝတီတိုင်းဒေသကြီး 9.4
2 2 Bago MMR111 Region ပဲခူးတိုင်းဒေသကြီး 9.4
3 4 Chin MMR004 State ချင်းပြည်နယ် 9.4
4 5 Kachin MMR001 State ကချင်ပြည်နယ် 9.4
5 6 Kayah MMR002 State ကယားပြည်နယ် 9.4
6 7 Kayin MMR003 State ကရင်ပြည်နယ် 9.4
7 8 Magway MMR009 Region မကွေးတိုင်းဒေသကြီး 9.4
8 9 Mandalay MMR010 Region မန္တလေးတိုင်းဒေသကြီး 9.4
9 10 Mon MMR011 State မွန်ပြည်နယ် 9.4
10 11 Nay Pyi Taw MMR018 Union Territory နေပြည်တော် 9.4
All_Fatalities geometry
1 57 MULTIPOLYGON (((93411.72 17...
2 364 MULTIPOLYGON (((203949.9 21...
3 1158 MULTIPOLYGON (((-72918.03 2...
4 756 MULTIPOLYGON (((362696.3 31...
5 1008 MULTIPOLYGON (((309155.7 22...
6 1021 MULTIPOLYGON (((373551.3 18...
7 2488 MULTIPOLYGON (((-1717.607 2...
8 1068 MULTIPOLYGON (((208184.3 26...
9 711 MULTIPOLYGON (((364238.4 16...
10 13 MULTIPOLYGON (((220155 2248...acled_mya_civiliansTargeted_by_admin1_2022 <- acled_mya %>%
filter(year == 2022) %>%
mutate(admin1 = case_when(
admin1 %in% c("Shan-North", "Shan-South", "Shan-East") ~ "Shan",
admin1 %in% c("Bago-East", "Bago-West") ~ "Bago",
TRUE ~ admin1
)) %>%
group_by(admin1) %>%
summarise(
count = n(),
Civilians_targeted = sum(civilian_targeting == "Civilian targeting", na.rm = TRUE)
)%>%
ungroup() %>%
mutate(`%_with_Civilian_Targeting` = (`Civilians_targeted` / `count`) * 100)
acled_mya_civiliansTargeted_by_admin1_2022# A tibble: 15 × 4
admin1 count Civilians_targeted `%_with_Civilian_Targeting`
<chr> <int> <int> <dbl>
1 Ayeyarwady 286 44 15.4
2 Bago 353 78 22.1
3 Chin 559 83 14.8
4 Kachin 589 126 21.4
5 Kayah 542 104 19.2
6 Kayin 631 114 18.1
7 Magway 1620 244 15.1
8 Mandalay 1315 264 20.1
9 Mon 523 99 18.9
10 Nay Pyi Taw 68 6 8.82
11 Rakhine 643 165 25.7
12 Sagaing 5725 775 13.5
13 Shan 950 254 26.7
14 Tanintharyi 958 185 19.3
15 Yangon 1192 256 21.5 combined <- left_join(mmr_admin1, acled_mya_civiliansTargeted_by_admin1_2022, by = c("ST" = "admin1"))
combinedSimple feature collection with 15 features and 9 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -210008.6 ymin: 1072026 xmax: 724647.6 ymax: 3158467
Projected CRS: WGS 84 / UTM zone 47N
First 10 features:
OBJECTID ST ST_PCODE ST_RG ST_MMR PCode_V count
1 1 Ayeyarwady MMR017 Region ဧရာဝတီတိုင်းဒေသကြီး 9.4 286
2 2 Bago MMR111 Region ပဲခူးတိုင်းဒေသကြီး 9.4 353
3 4 Chin MMR004 State ချင်းပြည်နယ် 9.4 559
4 5 Kachin MMR001 State ကချင်ပြည်နယ် 9.4 589
5 6 Kayah MMR002 State ကယားပြည်နယ် 9.4 542
6 7 Kayin MMR003 State ကရင်ပြည်နယ် 9.4 631
7 8 Magway MMR009 Region မကွေးတိုင်းဒေသကြီး 9.4 1620
8 9 Mandalay MMR010 Region မန္တလေးတိုင်းဒေသကြီး 9.4 1315
9 10 Mon MMR011 State မွန်ပြည်နယ် 9.4 523
10 11 Nay Pyi Taw MMR018 Union Territory နေပြည်တော် 9.4 68
Civilians_targeted %_with_Civilian_Targeting geometry
1 44 15.384615 MULTIPOLYGON (((93411.72 17...
2 78 22.096317 MULTIPOLYGON (((203949.9 21...
3 83 14.847943 MULTIPOLYGON (((-72918.03 2...
4 126 21.392190 MULTIPOLYGON (((362696.3 31...
5 104 19.188192 MULTIPOLYGON (((309155.7 22...
6 114 18.066561 MULTIPOLYGON (((373551.3 18...
7 244 15.061728 MULTIPOLYGON (((-1717.607 2...
8 264 20.076046 MULTIPOLYGON (((208184.3 26...
9 99 18.929254 MULTIPOLYGON (((364238.4 16...
10 6 8.823529 MULTIPOLYGON (((220155 2248...tm_shape(combined) +
tm_fill("%_with_Civilian_Targeting", n = 8, style = "kmeans") +
tm_borders(alpha = 0.5) +
tm_layout(title = "admin1 by % of incidents targeting civilians, 2022", legend.outside = TRUE)
The above map shows that the highest proportion of armed conflicts in the westernmost Rakhine state and easternmost Shan state involves violence against civilians. This state is the epicentre of the 2017 Rohingya crisis facing the eponymous Muslim ethnic group, discrimination against whom, in fact, goes as far back as the 1970s (Albert & Maizland, 2020). In joint second are the regions of Bago and Yangon.
TH1.5.2 Overarching Spatial-Temporal KDE
Next, we can try spatial-temporal kernel density estimation (STKDE) on the entire study area. As always, we start by creating ppp objects out of our sf objects, and checking for duplicates.
acled_mya_sf_quarters <- acled_mya_sf %>% select(Quarter_num)
acled_mya_quarters_ppp <- as.ppp(acled_mya_sf_quarters)
any(duplicated(acled_mya_quarters_ppp))[1] TRUESince there are duplicates, we need to handle them using jittering.
acled_mya_quarters_ppp <- rjitter(acled_mya_quarters_ppp, retry=TRUE, nsim=1, drop=TRUE)
any(duplicated(acled_mya_quarters_ppp))[1] FALSEFinally, we join the owin object mapping the borders of Myanmar to the ppp object.
acled_mya_quarters_ppp <- acled_mya_quarters_ppp[mmr_admin1_owin]
plot(acled_mya_quarters_ppp)
We can now perform spatial-temporal KDE (STKDE).
st_kde <- spattemp.density(acled_mya_quarters_ppp)Calculating trivariate smooth...Done.
Edge-correcting...Done.
Conditioning on time...Done.summary(st_kde)Spatiotemporal Kernel Density Estimate
Bandwidths
h = 42823.22 (spatial)
lambda = 0.0051 (temporal)
No. of observations
53562
Spatial bound
Type: polygonal
2D enclosure: [-210008.6, 724647.6] x [1072026, 3158467]
Temporal bound
[1, 15]
Evaluation
128 x 128 x 15 trivariate lattice
Density range: [9.772812e-26, 2.087501e-10]After performing STKDE, we are able to plot the quarterly STKDE objects between 2021 and September 2024. The full set of plots can be found in Annex A, but the plot for Q1 2021 (id 15) is of particular interest because of the larger than usual densities around the Sagaing and Chin areas, as well as near Yangon.
i = 15
plot(st_kde, quarter_numbers[i], override.par=FALSE, fix.range=TRUE, main=paste("Spatial-temporal KDE for", quarters[i]))
TH1.5.3 STKDE on Data Subset
Knowing that a significant part of the armed violence in Myanmar is due to systemic discrimination on the part of the government or military, it would make sense to repeat the process and plot STKDEs on the subset of the data concerning violence against civilians.
unique_type <- unique(acled_mya_sf$event_type)
unique_type[1] "Battles" "Explosions/Remote violence"
[3] "Strategic developments" "Protests"
[5] "Violence against civilians" "Riots" acled_sf_againstCivilians <- acled_mya_sf %>% filter(event_type == "Violence against civilians")
acled_sf_againstCiviliansSimple feature collection with 6398 features and 34 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: -206931.7 ymin: 1103500 xmax: 640934.5 ymax: 3042960
Projected CRS: WGS 84 / UTM zone 47N
# A tibble: 6,398 × 35
event_id_cnty event_date year time_precision disorder_type event_type
* <chr> <date> <dbl> <dbl> <chr> <chr>
1 MMR66362 2024-09-03 2024 1 Political violence Violence ag…
2 MMR66477 2024-09-03 2024 1 Political violence Violence ag…
3 MMR66468 2024-09-02 2024 1 Political violence Violence ag…
4 MMR66487 2024-09-02 2024 1 Political violence Violence ag…
5 MMR66475 2024-09-01 2024 1 Political violence Violence ag…
6 MMR66454 2024-09-01 2024 1 Political violence Violence ag…
7 MMR66447 2024-08-31 2024 1 Political violence Violence ag…
8 MMR66388 2024-08-31 2024 1 Political violence Violence ag…
9 MMR66485 2024-08-31 2024 1 Political violence Violence ag…
10 MMR66505 2024-08-30 2024 1 Political violence Violence ag…
# ℹ 6,388 more rows
# ℹ 29 more variables: sub_event_type <chr>, actor1 <chr>, assoc_actor_1 <chr>,
# inter1 <dbl>, actor2 <chr>, assoc_actor_2 <chr>, inter2 <dbl>,
# interaction <dbl>, civilian_targeting <chr>, iso <dbl>, region <chr>,
# country <chr>, admin1 <chr>, admin2 <chr>, admin3 <chr>, location <chr>,
# geo_precision <dbl>, source <chr>, source_scale <chr>, notes <chr>,
# fatalities <dbl>, tags <chr>, timestamp <dbl>, geometry <POINT [m]>, …acled_sf_againstCivilians <- acled_sf_againstCivilians %>% select(Quarter_num)acled_mya_againstCivilians_ppp <- as.ppp(acled_sf_againstCivilians)
any(duplicated(acled_mya_againstCivilians_ppp))[1] TRUEacled_mya_againstCivilians_ppp <- rjitter(acled_mya_againstCivilians_ppp, retry=TRUE, nsim=1, drop=TRUE)
any(duplicated(acled_mya_againstCivilians_ppp))[1] FALSEacled_mya_againstCivilians_ppp <- acled_mya_againstCivilians_ppp[mmr_admin1_owin]
plot(acled_mya_againstCivilians_ppp)
st_kde_againstCivilians <- spattemp.density(acled_mya_quarters_ppp)Calculating trivariate smooth...Done.
Edge-correcting...Done.
Conditioning on time...Done.summary(st_kde_againstCivilians)Spatiotemporal Kernel Density Estimate
Bandwidths
h = 42823.22 (spatial)
lambda = 0.0051 (temporal)
No. of observations
53562
Spatial bound
Type: polygonal
2D enclosure: [-210008.6, 724647.6] x [1072026, 3158467]
Temporal bound
[1, 15]
Evaluation
128 x 128 x 15 trivariate lattice
Density range: [9.772812e-26, 2.087501e-10]plot(st_kde_againstCivilians, quarter_numbers[15], override.par=FALSE, fix.range=TRUE, main=paste("Spatial-temporal KDE for violence against civilians events in quarter", quarters[i]))
TH1.6 Zooming in
The next step will be to perform spatial point pattern analysis (SPPA) and spatial-temporal point patterns analysis (STPPA). To do this effectively we need to select certain regions to analyse.
While not entirely effective, the preliminary KDE plot identified large clusters of armed conflict in the Yangon area in the south off the Andaman sea, as well as the Sagaing region and Chin state to the central-west. From the chropleth maps, Yangon was shown as the area with the second highest degree of civilian targeting, while Sagaing had the most fatalities from the conflict.
Therefore, it makes sense to focus on Sagaing, Shan, Rakhine and Chin. Additionally, an analysis on the Tanintharyi region will cover the Kra Isthmus, the narrow isthmus linking central Thailand to Peninsula Malaysia, to study the scale of the part of the conflict that is geographically closest to the more stable and prosperous Malaysia and Singapore.
TH1.7 Spatial Point Patterns Analysis
TH1.7.1 Extracting and plotting study areas
The first step is to extract the study areas and create owin objects.
study_names <- c("Sagaing", "Shan", "Rakhine", "Chin", "Yangon", "Tanintharyi")
study_areas <- list("Sagaing" = (mmr_admin1 %>% filter(ST == "Sagaing")),
"Shan" = (mmr_admin1 %>% filter(ST == "Shan")),
"Rakhine" = (mmr_admin1 %>% filter(ST == "Rakhine")),
"Chin" = (mmr_admin1 %>% filter(ST == "Chin")),
"Yangon" = (mmr_admin1 %>% filter(ST == "Yangon")),
"Tanintharyi" = (mmr_admin1 %>% filter(ST == "Tanintharyi"))
)
par(mfrow = c(1, 4))
for (study_name in study_names) {
print(paste("Study area:", study_name))
plot(study_areas[[study_name]], main = study_name)
}[1] "Study area: Sagaing"
[1] "Study area: Shan"
[1] "Study area: Rakhine"
[1] "Study area: Chin"
[1] "Study area: Yangon"
[1] "Study area: Tanintharyi"
study_ppps <- list()
par(mfrow = c(1, 3))
for (study_name in study_names) {
print(paste("Creating ppp for", study_name))
study_owin <- as.owin(study_areas[[study_name]])
study_ppps[[study_name]] <- rescale.ppp(acled_mya_quarters_ppp[study_owin], 1000, "km")
plot(study_ppps[[study_name]], main=paste("ppp for", study_name))
}[1] "Creating ppp for Sagaing"[1] "Creating ppp for Shan"[1] "Creating ppp for Rakhine"
[1] "Creating ppp for Chin"[1] "Creating ppp for Yangon"[1] "Creating ppp for Tanintharyi"
TH1.6.2 Fixed-bandwidth KDE
fixed_bandwidth_kde_acled <- list()
par(mfrow = c(1, 3))
for (study_name in study_names) {
fixed_bandwidth_kde_acled[[study_name]] <- density(study_ppps[[study_name]],
sigma=bw.scott,
edge=TRUE,
kernel="gaussian"
)
plot(fixed_bandwidth_kde_acled[[study_name]],
main = paste("Fixed-bandwidth KDE for", study_name))
}
TH1.6.3 Adaptive-bandwidth KDE
adaptive_bandwidth_kde_acled <- list()
par(mfrow = c(1, 3))
for (study_name in study_names) {
adaptive_bandwidth_kde_acled[[study_name]] <- adaptive.density(study_ppps[[study_name]],
method = "kernel"
)
plot(adaptive_bandwidth_kde_acled[[study_name]],
main = paste("Adaptive-bandwidth KDE for", study_name))
}
Fixed bandwidth KDE using bw.scott and the gaussian kernel returned a more meaningful result than adaptive bandwidth KDE.
Just for completeness, we will now attempt to map the results of fixed-bandwidth KDE for Rakhine state by creating a RasterLayer.
rakhine_kde <- fixed_bandwidth_kde_acled[["Rakhine"]]
gridded <- as(rakhine_kde, "SpatialGridDataFrame")
spplot(gridded)
raster <- raster(rakhine_kde)
projection(raster) <- CRS("+init=EPSG:32647")
tm_shape(raster) + tm_raster("layer", palette = "viridis") + tm_layout(legend.position = c("left", "bottom"), frame = FALSE)
TH1.6.4 Spatial-temporal KDE
We can also plot spatial-temporal KDEs for each of our selected regions. The full plots can be found in Annex B.
stkde_six_regions <- list()
for (study_name in study_names) {
print(study_name)
stkde_six_regions[[study_name]] <- spattemp.density(study_ppps[[study_name]])
}[1] "Sagaing"Calculating trivariate smooth...Done.
Edge-correcting...Done.
Conditioning on time...Done.[1] "Shan"Calculating trivariate smooth...Done.
Edge-correcting...Done.
Conditioning on time...Done.[1] "Rakhine"Calculating trivariate smooth...Done.
Edge-correcting...Done.
Conditioning on time...Done.[1] "Chin"Calculating trivariate smooth...Done.
Edge-correcting...Done.
Conditioning on time...Done.[1] "Yangon"Calculating trivariate smooth...Done.
Edge-correcting...Done.
Conditioning on time...Done.[1] "Tanintharyi"Calculating trivariate smooth...Done.
Edge-correcting...Done.
Conditioning on time...Done.TH1.6.5 Nearest neighbour analysis
For each of the 6 regions we are analysing, we can use nearest neighbour analysis to find out whether the spatial point patterns of our armed conflict events exhibit clustering, dispersion or random distribution. The nearest neighbour analysis is done by performing the Clark-Evans test on the hypothesis that the distribution of armed conflict events is random.
for (study_name in study_names) {
print(study_name)
ppp <- study_ppps[[study_name]]
# plot(ppp)
print(clarkevans.test(ppp, correction = "none", clipregion = NULL, alternative = c("two.sided"), nsim = 99))
}[1] "Sagaing"
Clark-Evans test
No edge correction
Z-test
data: ppp
R = 0.16523, p-value < 2.2e-16
alternative hypothesis: two-sided
[1] "Shan"
Clark-Evans test
No edge correction
Z-test
data: ppp
R = 0.16189, p-value < 2.2e-16
alternative hypothesis: two-sided
[1] "Rakhine"
Clark-Evans test
No edge correction
Z-test
data: ppp
R = 0.21369, p-value < 2.2e-16
alternative hypothesis: two-sided
[1] "Chin"
Clark-Evans test
No edge correction
Z-test
data: ppp
R = 0.17452, p-value < 2.2e-16
alternative hypothesis: two-sided
[1] "Yangon"
Clark-Evans test
No edge correction
Z-test
data: ppp
R = 0.13365, p-value < 2.2e-16
alternative hypothesis: two-sided
[1] "Tanintharyi"
Clark-Evans test
No edge correction
Z-test
data: ppp
R = 0.12985, p-value < 2.2e-16
alternative hypothesis: two-sidedTH1.6.5 Second order spatial point patterns analysis
TH1.6.5.1 F-function
TH1.6.5.2 G-function
TH1.6.5.3 K-function
TH1.6.5.4 L-function
TH1.7 Rakhine State
TH1.7.1 Fixed-bandwidth KDE
TH1.7.2 Adaptive-bandwidth KDE
TH1.7.3 Nearest neighbour analysis
TH1.7.4 Second order spatial point patterns analysis
TH1.7.4.1 F-function
TH1.7.4.2 G-function
TH1.7.4.3 K-function
TH1.7.4.4 L-function
References
- Albert, E., & Maizland, L. (2020, January 23). What forces are fueling Myanmar’s Rohingya crisis? Council on Foreign Relations. https://www.cfr.org/backgrounder/rohingya-crisis
- Maizland, L. (2022, January 31). Myanmar’s troubled history: Coups, military rule, and ethnic conflict. Council on Foreign Relations. https://www.cfr.org/backgrounder/myanmar-history-coup-military-rule-ethnic-conflict-rohingya
- Myanmar Information Management Unit. (n.d.). Country Overview. Retrieved 16 September 2024, from https://www.themimu.info/country-overview
- Raleigh, C., Kishi, R. & Linke, A. Political instability patterns are obscured by conflict dataset scope conditions, sources, and coding choices. Humanit Soc Sci Commun 10, 74 (2023). https://doi.org/10.1057/s41599-023-01559-4
- Rieffel, L. (2019, December 6). Peace and war in Myanmar. Brookings. https://www.brookings.edu/articles/peace-and-war-in-myanmar/
- Smith, M. (1999). Burma: Insurgency and the politics of ethnicity (2. (updated) ed). Univ. Press [u.a.].
- Sullivan, D. P. (2021, October 21). Dire consequences: Addressing the humanitarian fallout from myanmar’s coup. Refugees International; Refugees International. https://www.refugeesinternational.org/reports-briefs/dire-consequences-addressing-the-humanitarian-fallout-from-myanmars-coup/
- UNDP (Ed.). (2024). Breaking the gridlock: Reimagining cooperation in a polarized world. United Nations Development Programme (UNDP).
Annex A: Full Quarterly STKDE Plots for Jan 2021- Sep 2024
A1: Full STKDE Plots
for (i in 1: length(quarter_numbers)) {
plot(st_kde, quarter_numbers[i], override.par=FALSE, fix.range=TRUE, main=paste("Spatial-temporal KDE for", quarters[i]))
}
A2: For events with civilian targeting only
for (i in 1: length(quarter_numbers)) {
plot(st_kde_againstCivilians, quarter_numbers[i], override.par=FALSE, fix.range=TRUE, main=paste("Spatial-temporal KDE for violence against civilians events in quarter", quarters[i]))
}
Annex B: Region-specific STKDE plots
for (study_name in study_names) {
for (i in 1: length(quarter_numbers)) {
print(study_name)
print(quarter_numbers[i])
plot(stkde_six_regions[[study_name]], quarter_numbers[i], override.par=FALSE, fix.range=TRUE, main=paste("STKDE for", study_name , "in quarter", quarters[i]))
}
}[1] "Sagaing"
[1] 15
[1] "Sagaing"
[1] 14
[1] "Sagaing"
[1] 13
[1] "Sagaing"
[1] 12
[1] "Sagaing"
[1] 11
[1] "Sagaing"
[1] 10
[1] "Sagaing"
[1] 9
[1] "Sagaing"
[1] 8
[1] "Sagaing"
[1] 7
[1] "Sagaing"
[1] 6
[1] "Sagaing"
[1] 5
[1] "Sagaing"
[1] 4
[1] "Sagaing"
[1] 3
[1] "Sagaing"
[1] 2
[1] "Sagaing"
[1] 1
[1] "Shan"
[1] 15
[1] "Shan"
[1] 14
[1] "Shan"
[1] 13
[1] "Shan"
[1] 12
[1] "Shan"
[1] 11
[1] "Shan"
[1] 10
[1] "Shan"
[1] 9
[1] "Shan"
[1] 8
[1] "Shan"
[1] 7
[1] "Shan"
[1] 6
[1] "Shan"
[1] 5
[1] "Shan"
[1] 4
[1] "Shan"
[1] 3
[1] "Shan"
[1] 2
[1] "Shan"
[1] 1
[1] "Rakhine"
[1] 15
[1] "Rakhine"
[1] 14
[1] "Rakhine"
[1] 13
[1] "Rakhine"
[1] 12
[1] "Rakhine"
[1] 11
[1] "Rakhine"
[1] 10
[1] "Rakhine"
[1] 9
[1] "Rakhine"
[1] 8
[1] "Rakhine"
[1] 7
[1] "Rakhine"
[1] 6
[1] "Rakhine"
[1] 5
[1] "Rakhine"
[1] 4
[1] "Rakhine"
[1] 3
[1] "Rakhine"
[1] 2
[1] "Rakhine"
[1] 1
[1] "Chin"
[1] 15
[1] "Chin"
[1] 14
[1] "Chin"
[1] 13
[1] "Chin"
[1] 12
[1] "Chin"
[1] 11
[1] "Chin"
[1] 10
[1] "Chin"
[1] 9
[1] "Chin"
[1] 8
[1] "Chin"
[1] 7
[1] "Chin"
[1] 6
[1] "Chin"
[1] 5
[1] "Chin"
[1] 4
[1] "Chin"
[1] 3
[1] "Chin"
[1] 2
[1] "Chin"
[1] 1
[1] "Yangon"
[1] 15
[1] "Yangon"
[1] 14
[1] "Yangon"
[1] 13
[1] "Yangon"
[1] 12
[1] "Yangon"
[1] 11
[1] "Yangon"
[1] 10
[1] "Yangon"
[1] 9
[1] "Yangon"
[1] 8
[1] "Yangon"
[1] 7
[1] "Yangon"
[1] 6
[1] "Yangon"
[1] 5
[1] "Yangon"
[1] 4
[1] "Yangon"
[1] 3
[1] "Yangon"
[1] 2
[1] "Yangon"
[1] 1
[1] "Tanintharyi"
[1] 15
[1] "Tanintharyi"
[1] 14
[1] "Tanintharyi"
[1] 13
[1] "Tanintharyi"
[1] 12
[1] "Tanintharyi"
[1] 11
[1] "Tanintharyi"
[1] 10
[1] "Tanintharyi"
[1] 9
[1] "Tanintharyi"
[1] 8
[1] "Tanintharyi"
[1] 7
[1] "Tanintharyi"
[1] 6
[1] "Tanintharyi"
[1] 5
[1] "Tanintharyi"
[1] 4
[1] "Tanintharyi"
[1] 3
[1] "Tanintharyi"
[1] 2
[1] "Tanintharyi"
[1] 1
— OLD —
TH1.5 First Order SPPA - KDE
Kernel density estimation (KDE) serves to compute the intensity of a point distribution. It has two general steps: first to compute the point intensity, followed by spatial interpolation using a kernel function. Using KDE, we can determine the general region of Myanmar with the highest incidence of armed conflict.
TH1.5.1 Overall Fixed-Bandwidth KDE
TH1.5.2 Overall adaptive-bandwidth KDE
TH1.5.3 Quarterly fixed-bandwidth KDE
TH1.5.4 Quarterly adaptive-bandwidth KDE
TH1.6 First Order STPPA: Spatio-temporal KDE
TH1.7 First-order SPPA: Nearest Neighbour Analysis
To determine whether the plotted armed conflict events are clustered, dispersed or random, we can use the Clark-Evans test. Since tensions between Myanmar’s different ethnic groups already existed before the 2021 coup, it would be a good idea to run the test on the subsets of data both before and after the coup.
TH1.7 Second-order SPPA
Second order SPPA deals with variations in observations due to the way they interact with one another. The methods used for second order SPPA are the F-, G-, K- and L-functions. In this section, we will perform second-order SPPA on the conflict dataset, applying the F-, G-, K- and L-functions on subsets of the data before and after the 2021 coup.